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Item Measuring Performance of United States Commercial and Domestic Banks and its Impact on 2007-2009 Financial Crisis(North Dakota State University, 2019) Sakouvogui, KekouraIn the analysis of efficiency measures, the statistical Stochastic Frontier Analysis (SFA) and linear programming Data Envelopment Analysis (DEA) estimators have been widely applied. This dissertation is centered around two main goals. First, this dissertation addresses respectively the individual limitations of SFA and DEA models in chapters 2 and 3 using Monte Carlo (MC) simulations. Motivated by the lack of justification for the choice of inefficiency distributions in MC simulations, chapter 2 develops the statistical parameters, i.e., mean and standard deviation of the inefficiency distributions - truncated normal, half normal, and exponential. MC simulations results show that within the conventional and proposed approaches, misspecification of the inefficiency distribution matters. More precisely, within the proposed approach, the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the inefficiency distribution is a half normal. Chapter 3 examines several misspecifications of the DEA efficiency measures while accounting for the stochastic inefficiency distributions of truncated normal, half normal, and exponential derived in chapter 2. MC simulations were conducted to examine the performance of the DEA model under two different data generating processes - logarithm and level, and across five different scenarios - inefficiency distributions, sample sizes, production functions, input distributions, and curse of dimensionality. The results caution DEA practitioners concerning the accuracy of their estimates and the implications within proposed and conventional approaches of the inefficiency distributions. Second, this dissertation presents in chapter 4 an empirical assessment of the liquidity and solvency financial factors on the cost efficiency measures of U.S banks while accounting for regulatory, macroeconomic, and bank internal factors. The results suggest that the liquidity and solvency financial factors negatively impacted the cost efficiency measures of U.S banks from 2005 to 2017. Moreover, during the financial crisis, U.S banks were inefficient in comparison to the tranquil period, and the solvency financial factor insignificantly impacted the cost efficiency measures. In addition, U.S banks’ liquidity financial factor negatively collapsed due to contagion during the financial crisis.Item Robust Capital Asset Pricing Model Estimation through Cross-Validation(North Dakota State University, 2018) Sakouvogui, KekouraLimitations of Capital Asset Pricing Model (CAPM) continue to present inconsistent empirical results despite its rm mathematical foundations provided in recent studies. In this thesis, we examine how estimation errors of the CAPM could be minimized using the cross-validation technique, a concept that is widely applied in machine learning (CV-CAPM). We apply our approach to test the assumption of CAPM as a well-diversified portfolio model with data from S&P500 and Dow Jones Industrial Average (DJIA). Our results from the CV-CAPM validate that both S&P500 and DJIA are well-diversified market indices with statistically insignificant variation in unsystematic risks during and after the 2007 financial crisis. Furthermore, the CV-CAPM provides the smallest root mean square errors and mean absolute deviations compared to the traditional CAPM.Item Comparative Classification of Prostate Cancer Data using the Support Vector Machine, Random Forest, Dualks and k-Nearest Neighbours(North Dakota State University, 2015) Sakouvogui, KekouraThis paper compares four classifications tools, Support Vector Machine (SVM), Random Forest (RF), DualKS and the k-Nearest Neighbors (kNN) that are based on different statistical learning theories. The dataset used is a microarray gene expression of 596 male patients with prostate cancer. After treatment, the patients were classified into one group of phenotype with three levels: PSA (Prostate-Specific Antigen), Systematic and NED (No Evidence of Disease). The purpose of this research is to determine the performance rate of each classifier by selecting the optimal kernels and parameters that give the best prediction rate of the phenotype. The paper begins with the discussion of previous implementations of the tools and their mathematical theories. The results showed that three classifiers achieved a comparable performance that was above the average while DualKS did not. We also observed that SVM outperformed the kNN, RF and DualKS classifiers.